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Learning machine learning

Published:20 November 2018Publication History
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Abstract

A discussion of the rapidly evolving realm of machine learning.

References

  1. Cybenko, G. Approximation by superpositions of a Sigmoid function. Math. Control Signals Systems 2, (1989), 303--314.Google ScholarGoogle ScholarCross RefCross Ref
  2. Hopfield, J. and Tank, D. Neural computation of decisions in optimization problems. Biological Cybernetics 52 (1985), 141--152. Google ScholarGoogle ScholarCross RefCross Ref
  3. Hornik, K., Stinchcombe, M., and White, H. Multilayer feedforward networks are universal approximators. Neural Networks 3, 2 (1989), 359--366. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. McCulloch, W.S. and Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115 (1943)Google ScholarGoogle ScholarCross RefCross Ref
  5. Minsky, M. and Papert, S. Perceptrons. MIT Press, 1969.Google ScholarGoogle Scholar

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  1. Learning machine learning

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    • Published in

      cover image Communications of the ACM
      Communications of the ACM  Volume 61, Issue 12
      December 2018
      104 pages
      ISSN:0001-0782
      EISSN:1557-7317
      DOI:10.1145/3293542
      Issue’s Table of Contents

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 20 November 2018

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